Reducing Line-of-Block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods

Schuler, Steffen; Schaufelberger, Matthias; Bear, Laura; Bergquist, Jake A; Cluitmans, Matthijs; Coll‐Font, Jaume; Onak, Önder Nazım; Zenger, Brian; Loewe, Axel; MacLeod, Robert S.; Brooks, Dana H.; Doessel, Olaf

doi:10.1109/tbme.2021.3135154

Cited by 14 publications

(12 citation statements)

References 49 publications

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“…ECGI has been formulated in terms of various equivalent cardiac source models, including single/multiple equivalent dipoles or equivalent double-layer (EDL) representation (Zhou et al, 2019;Svehlikova et al, 2022), activation isochrones (Erem et al, 2014b;Potyagaylo et al, 2019b), transmural or heart surface transmembrane potentials (Wang et al, 2011;Rahimi et al, 2016;Schuler et al, 2022), and extracellular potentials on the closed epicardial ("Epi") or combined epicardial/endocardial ('EpiEndo') heart surface (Bear et al, 2019;Erenler and Serinagaoglu Dogrusoz, 2019;Onak et al, 2022;Schuler et al, 2022). In this study, we compare two well-established cardiac source models for PVC localization: a single equivalent dipole-based source model and the model in terms of extracellular potentials defined on the heart surface (Epi and EpiEndo), which is a monopole-based source model coupled with BEM.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, PVC localization from the potential-based approach requires finding the activation times (AT) and determining the earliest activated location. These post-processing steps could introduce artifacts in the reconstructed activation isochrones, as demonstrated in Schuler et al (2022), which could cause errors in the PVC location estimates.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Doğrusöz¹,

Rasoolzadeh²,

Ondrusova³

et al. 2023

Front. Physiol.

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Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances.Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial–endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated.Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2–33.0 mm for the dipole-based model and 28.9–39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial–endocardial ventricular surface.Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Doğrusöz¹,

Rasoolzadeh²,

Ondrusova³

et al. 2023

Front. Physiol.

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“…ATs were derived from ECGI and sock EGMs as the time of the minimum dV/dt. For ECGI, the ATs were then spatially smoothed by combining them with an estimate of the local spatial gradient ( Cluitmans et al., 2022 ), a method well known to increase the accuracy of the reconstructed ATs over the traditional temporal-only method that are more prone to lines of false conduction block and poor estimates for ectopic localization ( Cluitmans et al., 2017 ; Duchateau et al., 2019 ; Schuler et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation

et al. 2023

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Introduction: Premature ventricular contractions (PVCs) are one of the most commonly targeted pathologies for ECGI validation, often through ventricular stimulation to mimic the ectopic beat. However, it remains unclear if such stimulated beats faithfully reproduce spontaneously occurring PVCs, particularly in the case of the R-on-T phenomenon. The objective of this study was to determine the differences in ECGI accuracy when reconstructing spontaneous PVCs as compared to ventricular-stimulated beats and to explore the impact of pathophysiological perturbation on this reconstruction accuracy.Methods: Langendorff-perfused pig hearts (n = 3) were suspended in a human torso-shaped tank, and local hyperkalemia was induced through perfusion of a high-K+ solution (8 mM) into the LAD. Recordings were taken simultaneously from the heart and tank surfaces during ventricular pacing and during spontaneous PVCs (including R-on-T), both at baseline and high K+. Epicardial potentials were reconstructed from torso potentials using ECGI.Results: Spontaneously occurring PVCs were better reconstructed than stimulated beats at baseline in terms of electrogram morphology [correlation coefficient (CC) = 0.74 ± 0.05 vs. CC = 0.60 ± 0.10], potential maps (CC = 0.61 ± 0.06 vs. CC = 0.51 ± 0.12), and activation time maps (CC = 0.86 ± 0.07 vs. 0.76 ± 0.10), though there was no difference in the localization error (LE) of epicardial origin (LE = 14 ± 6 vs. 15 ± 11 mm). High K+ perfusion reduced the accuracy of ECGI reconstructions in terms of electrogram morphology (CC = 0.68 ± 0.10) and AT maps (CC = 0.70 ± 0.12 and 0.59 ± 0.23) for isolated PVCs and paced beats, respectively. LE trended worse, but the change was not significant (LE = 17 ± 9 and 20 ± 12 mm). Spontaneous PVCs were less well when the R-on-T phenomenon occurred and the activation wavefronts encountered a line of block.Conclusion: This study demonstrates the differences in ECGI accuracy between spontaneous PVCs and ventricular-paced beats. We also observed a reduction in this accuracy near regions of electrically inactive tissue. These results highlight the need for more physiologically realistic experimental models when evaluating the accuracy of ECGI methods. In particular, reconstruction accuracy needs to be further evaluated in the presence of R-on-T or isolated PVCs, particularly when encountering obstacles (functional or anatomical) which cause line of block and re-entry.

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“…Excitation propagation simulations (Fig. 1D) were performed with an anisotropic eikonal model as used in [54],…”

Section: Excitation Propagation Simulationsmentioning

confidence: 99%

Non-invasive Localization of the Ventricular Excitation Origin Without Patient-specific Geometries Using Deep Learning

Pilia¹,

Schuler²,

Rees³

et al. 2022

Preprint

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Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This kind of arrhythmia can be caused by focal activation sources outside the sinus node. A curative treatment is catheter ablation of these foci in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) addressing these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Furthermore, one of the method's outputs could be interpreted as several ranked solutions. The CNNs were trained on a data set containing only simulated data and evaluated both on simulated and clinical test data. On simulated data, the median test error was below 3 mm. The median localization error on the unseen clinical (test) data was between 32 mm and 41 mm without optimizing the pre-processing and CNN on the clinical (test) data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions dropped to 20 mm on the clinical test set. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need of patient-specific geometrical information. Furthermore, delivering multiple solutions can help the physician to find the real activation source amongst more than one possible locations. With a further optimization to clinical data, these methods have a high potential to speed up clinical interventions, replace certain steps within those and therefore consequently decrease procedural risk and improve VT patients' outcomes.

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Reducing Line-of-Block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods

Cited by 14 publications

References 49 publications

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation

Non-invasive Localization of the Ventricular Excitation Origin Without Patient-specific Geometries Using Deep Learning

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